Large language models (LLMs) have introduced new possibilities for prototyping with AI [1]}.
Pre-trained on a large amount of text data, models like GPT-3 [2]} and Jurassic-1 [3]} encode enough information to support in-context learning:
they can be easily customized at run time (without any re-training needed) to handle new tasks, simply by taking in natural language instructions called prompts.
For example, a user could customize a pre-trained, general purpose LLM to create an ad-hoc search engine for musicians by giving it the prompt string: “Genre: Jazz; Artist: Louis Armstrong. Genre: Country; Artist: ”. An LLM would likely continue the prompt with the name of a country artist, e.g. “Garth Brooks.”
Beyond this toy example, non-ML experts have used prompting to achieve various ML functionalities in real-time, including code generation, question answering, creative writing, etc. [4]}, [5]}, [2]}.
Recent work on prompt-based prototyping [7]} found that, with LLMs' fluid adaptation to natural language prompts, non-ML experts (e.g. designers, product managers, front-end developers) can now prototype custom ML functionality with lower effort and less time, as they bypass the otherwise necessary but expensive process of collecting data and training models upfront [7]}, [9]}.
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